Hydrogels for Salivary Gland Tissue Engineering
Bibliographic record
Abstract
Mimicking the complex architecture of salivary glands (SGs) outside their native niche is challenging due their multicellular and highly branched organization. However, significant progress has been made to recapitulate the gland structure and function using several in vitro and ex vivo models. Hydrogels are polymers with the potential to retain a large volume of water inside their three-dimensional structure, thus simulating extracellular matrix properties that are essential for the cell and tissue integrity. Hydrogel-based culture of SG cells has seen a tremendous success in terms of developing platforms for cell expansion, building an artificial gland, and for use in transplantation to rescue loss of SG function. Both natural and synthetic hydrogels have been used widely in SG tissue engineering applications owing to their properties that support the proliferation, reorganization, and polarization of SG epithelial cells. While recent improvements in hydrogel properties are essential to establish more sophisticated models, the emphasis should still be made towards supporting factors such as mechanotransduction and associated signaling cues. In this concise review, we discuss considerations of an ideal hydrogel-based biomaterial for SG engineering and their associated signaling pathways. We also discuss the current advances made in natural and synthetic hydrogels for SG tissue engineering applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".